Fourier neural operator with boundary conditions for efficient prediction of steady airfoil flows
出版年份 2023 全文链接
标题
Fourier neural operator with boundary conditions for efficient prediction of steady airfoil flows
作者
关键词
-
出版物
APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION
Volume 44, Issue 11, Pages 2019-2038
出版商
Springer Science and Business Media LLC
发表日期
2023-10-31
DOI
10.1007/s10483-023-3050-9
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- An artificial viscosity augmented physics-informed neural network for incompressible flow
- (2023) Yichuan He et al. APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION
- Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
- (2023) Steven Guan et al. Algorithms
- U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow
- (2022) Gege Wen et al. ADVANCES IN WATER RESOURCES
- Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
- (2022) Hamidreza Eivazi et al. PHYSICS OF FLUIDS
- Fourier neural operator approach to large eddy simulation of three-dimensional turbulence
- (2022) Zhijie Li et al. Theoretical and Applied Mechanics Letters
- Data-driven parametric soliton-rogon state transitions for nonlinear wave equations using deep learning with Fourier neural operator
- (2022) Ming Zhong et al. COMMUNICATIONS IN THEORETICAL PHYSICS
- Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils
- (2021) Kai Li et al. AEROSPACE SCIENCE AND TECHNOLOGY
- When and why PINNs fail to train: A neural tangent kernel perspective
- (2021) Sifan Wang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Fast pressure distribution prediction of airfoils using deep learning
- (2020) Xinyu Hui et al. AEROSPACE SCIENCE AND TECHNOLOGY
- A physics-informed operator regression framework for extracting data-driven continuum models
- (2020) Ravi G. Patel et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Non-invasive inference of thrombus material properties with physics-informed neural networks
- (2020) Minglang Yin et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Prediction of aerodynamic flow fields using convolutional neural networks
- (2019) Saakaar Bhatnagar et al. COMPUTATIONAL MECHANICS
- Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows
- (2019) Nils Thuerey et al. AIAA JOURNAL
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- (2019) Luning Sun et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Physics-informed neural networks for high-speed flows
- (2019) Zhiping Mao et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils
- (2019) Haizhou Wu et al. COMPUTERS & FLUIDS
- Data-assisted reduced-order modeling of extreme events in complex dynamical systems
- (2018) Zhong Yi Wan et al. PLoS One
- Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
- (2018) Pantelis R. Vlachas et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Deep learning of vortex-induced vibrations
- (2018) Maziar Raissi et al. JOURNAL OF FLUID MECHANICS
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
- (2011) G. E. Dahl et al. IEEE Transactions on Audio Speech and Language Processing
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now